code: def test_predictor_working(
      predictor: StatsForecastPredictor, dataset: Dataset
  ):
      with TemporaryDirectory() as temp_dir:
          predictor.serialize(Path(temp_dir))
          predictor_copy = Predictor.deserialize(Path(temp_dir))
  
      assert predictor_copy == predictor
      assert isinstance(predictor_copy.model, predictor_copy.ModelType)
  
      for forecast in predictor_copy.predict(dataset):
          assert isinstance(forecast, QuantileForecast)
          assert len(forecast.mean) == predictor_copy.prediction_length
          assert (
              predictor_copy.config.forecast_keys
              == forecast.forecast_keys
          )
  
file_path: gluonts/test/ext/statsforecast/test_statsforecast.py
imports: from pathlib import Path
  from tempfile import TemporaryDirectory
  import pandas
  import pytest
  import numpy
  from gluonts.dataset import Dataset
  from gluonts.model import Predictor, QuantileForecast
  from gluonts.ext.statsforecast import ModelConfig, StatsForecastPredictor, ADIDAPredictor, AutoARIMAPredictor, AutoCESPredictor, AutoETSPredictor, AutoThetaPredictor, CrostonClassicPredictor, CrostonOptimizedPredictor, CrostonSBAPredictor, DynamicOptimizedThetaPredictor, DynamicThetaPredictor, HistoricAveragePredictor, HoltPredictor, HoltWintersPredictor, IMAPAPredictor, MSTLPredictor, NaivePredictor, OptimizedThetaPredictor, RandomWalkWithDriftPredictor, SeasonalExponentialSmoothingOptimizedPredictor, SeasonalExponentialSmoothingPredictor, SeasonalNaivePredictor, SeasonalWindowAveragePredictor, SimpleExponentialSmoothingOptimizedPredictor, SimpleExponentialSmoothingPredictor, TSBPredictor, ThetaPredictor, WindowAveragePredictor
label_desc: Use image node_id 2 for calling a global function with example usage: test_predictor_working(predictor, dataset) without return types